CAS-Climate: Understanding the Changing Climatology, Organizing Patterns and Source Attribution of Hazards of Floods over the Southcentral and Southeast US

洪水常常导致重大的生命和财产损失。该项目将重点了解流域条件以及大气和海洋条件如何导致美国中南部和东南部地区 (SESC) 每月发生洪水，因为该地区全年容易遭受洪水袭击。鉴于热带风暴的强度和频率不断增加（2020 年创下了大西洋 30 场命名风暴和 12 场在 SESC 登陆的记录），该项目将增强对洪水灾害的科学认识，也将更好地为更广泛的预报员和决策者提供信息。例如，最近的五场重大飓风——马修（2016 年）、哈维（2017 年）、艾尔玛（2017 年）、佛罗伦萨（2018 年）和艾达（2021 年）——导致 SESC 发生灾难性洪水，给地方、州和联邦机构的准备和恢复带来重大挑战。该项目团队将与气候办公室、北卡罗来纳大学阿什维尔国家环境建模和分析中心（NEMAC）以及国家环境信息中心（NCEI）合作。该项目团队还将与少数族裔服务机构的教师和研究生合作，作为北卡罗来纳州立大学暑期实习计划的一部分。该项目的结果将通过同行评审的出版物、研讨会和讲习班进行传播。该提案的目的是通过（a）量化气候学的变化，（b）确定其水分输送路径，以及（c）归因调节其时空变化的来源（陆地表面、大气和海洋），以增进对美国SESC每月洪水动态的了解。项目团队将研究 SESC 上的水文气候数据网络 (HCDN) 流域的气候变化和每月每日/三天最大流量的年际变化。主要研究人员还将使用各种观测和再分析数据、气候指数以及统计和物理（数值天气预报）模型。将使用最佳气候正常和“铰链拟合”方法来量化气候学的转变，并将使用多个拉格朗日粒子跟踪模型来分析组织和水分输送模式。将使用严格的统计技术（例如随机森林、铰链拟合）和物理建模来量化与每月洪水气候学相关的来源归属。贝叶斯分层模型 (BHM) 将结合五个已确定来源的贡献 – 1) 气候变化和水分传输来源，2) 遥相关，3) 大气河流 (AR)，4) 热带气旋 (TC) 和 5) 初始地表条件 – 影响多个空间尺度的洪水过程，以解释 SESC 地区观测到的每月洪水变化。该奖项反映了 NSF 的法定使命，并被认为值得通过评估支持利用基金会的智力优势和更广泛的影响审查标准。

Floods often lead to significant loss of life and property. This project will focus on understanding how watershed conditions and atmospheric and oceanic conditions cause monthly flooding over the Southcentral and Southeast US region (SESC) as the region is vulnerable to flooding throughout the year. Given the increasing strength and frequency of tropical storms (2020 set a record with 30 named storms in the Atlantic and 12 making landfall in the SESC), this project will enhance the scientific understanding of flood hazards and will also better inform the wider community of forecasters and decision makers. For instance, five recent major hurricanes – Matthew (2016), Harvey (2017), Irma (2017), Florence (2018), and Ida (2021) – led to catastrophic flooding over the SESC causing major challenges in preparedness and recovery for local, state and federal agencies. The project team will engage with climate offices, National Environmental Modeling and Analysis Center (NEMAC) at UNC, Asheville and National Centers for Environmental Information (NCEI). The project team will also collaborate with faculty and graduate students from minority-serving institutions as part of the summer internship program at NC State University. Results from the project will be disseminated through peer reviewed publications, seminars and workshops.The objective of this proposal is to improve understanding of monthly flood dynamics over the SESC US by (a) quantifying the shift in climatology, (b) identifying their moisture delivery pathways, and (c) attributing the sources (land surface, atmosphere and ocean) that modulate their spatiotemporal variability. The project team will examine the shift in climatology and interannual variability of monthly daily/3-day maximum streamflow in Hydroclimatic Data Network (HCDN) basins over the SESC. The principal investigators will also use a variety of observed and reanalysis data, climatic indices, as well as statistical and physical (numerical weather prediction) models. The shift in climatology will be quantified using optimal climate normal and “hinge fit” methodologies, and the organizational and moisture delivery patterns will be analyzed using multiple Lagrangian particle tracking models. Attribution of sources related to monthly flood climatology will be quantified using rigorous statistical techniques (e.g., random forest, hinge fit) and through physical modeling. A Bayesian Hierarchical Model (BHM) will combine the contribution from five identified sources – 1) shift in climatology and sources of moisture transport, 2) teleconnections, 3) atmospheric rivers (ARs), 4) tropical cyclones (TCs), and 5) initial land-surface conditions – that influence flood processes over multiple spatial scales for explaining the observed monthly flood variability in the SESC region.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.